Build CNN using Transfer Learning

Introduction

A powerful and widely-used approach to deep learning on small image datasets is leveraging pre-trained models. When the original dataset is sufficiently large and diverse, the features learned by a pre-trained model can serve as a generic representation of the visual world. These features can prove beneficial for various computer vision problems, even when dealing with different classes than those in the original task.

There are two primary methods for utilizing a pre-trained model:

1. Feature Extraction:

   • Extract meaningful features from the pre-trained model, freezing its weights, and using these features as input for a new classifier.

2. Fine-Tuning:

   • Fine-tune the top layers of the pre-trained model to adapt it to the specific characteristics of the new dataset.

Learning Objectives

Upon completing this project, you will achieve the following learning objectives:

• Understand and Use a Pre-trained Model:

  • Gain knowledge of using pre-trained models and extracting meaningful features from them.

• Fine-tune the Top Layers:

  • Learn the process of fine-tuning the top layers of a pre-trained model to adapt it for a specific image classification task.

Files Contained in the Project:

• CNN_Transfer_Learning.ipynb: Jupyter notebook containing the project implementation and experimentation.

Usage:

1. Clone the repository:

git clone https://github.com/Praveen76/Build-CNN-using-Transfer-Learning.git
cd Build-CNN-using-Transfer-Learning

2. Open and explore the Jupyter notebook CNN_Transfer_Learning.ipynb to understand the project's implementation.

3. Execute the code cells within the notebook to experiment with building a CNN using transfer learning, both through feature extraction and fine-tuning.

4. Gain practical experience in utilizing pre-trained models for image classification tasks.

Feel free to contribute, report issues, or suggest improvements!

Contributing

If you have a Data Science mini-project that you'd like to share, please follow the guidelines in CONTRIBUTING.md.

Code of Conduct

Please adhere to our Code of Conduct in all your interactions with the project.

License

This project is licensed under the MIT License.

Contact

For questions or inquiries, feel free to contact me on Linkedin.

About Me:

I’m a seasoned Data Scientist and founder of TowardsMachineLearning.Org. I've worked on various Machine Learning, NLP, and cutting-edge deep learning frameworks to solve numerous business problems.